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    Efficient algorithms for robust recovery of images from compressed data

    194364_194364.pdf (381.8Kb)
    Access Status
    Open access
    Authors
    Pham, DucSon
    Venkatesh, S.
    Date
    2013
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Pham, Duc-Son and Venkatesh, Svetha. 2013. Efficient algorithms for robust recovery of images from compressed data. IEEE Transactions on Image Processing. 22 (12): pp. 4724-4737.
    Source Title
    IEEE Transactions on Image Processing
    DOI
    10.1109/TIP.2013.2277821
    ISSN
    1057-7149
    School
    School of Electrical Engineering and Computing
    Remarks

    Copyright © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/17178
    Collection
    • Curtin Research Publications
    Abstract

    Compressed sensing (CS) is an important theory for sub-Nyquist sampling and recovery of compressible data. Recently, it has been extended to cope with the case where corruption to the CS data is modelled as impulsive noise. The new formulation, termed as robust CS, combines robust statistics and CS into a single framework to suppress outliers in the CS recovery. To solve the newly formulated robust CS problem, a scheme that iteratively solves a number of CS problems-the solutions from which provably converge to the true robust CS solution-is suggested. This scheme is, however, rather inefficient as it has to use existing CS solvers as a proxy. To overcome limitations with the original robust CS algorithm, we propose in this paper more computationally efficient algorithms by following latest advances in large-scale convex optimization for nonsmooth regularization. Furthermore, we also extend the robust CS formulation to various settings, including additional affine constraints, l1-norm loss function, mix-norm regularization, and multitasking, so as to further improve robust CS and derive simple but effective algorithms to solve these extensions. We demonstrate that the new algorithms provide much better computational advantage over the original robust CS method on the original robust CS formulation, and effectively solve more sophisticated extensions where the original methods simply cannot. We demonstrate the usefulness of the extensions on several imaging tasks.

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